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    <meta name="description" content="Hive本身支持好多种数据格式，其中OrcFile是当前压缩比最大的一个。因此进行该方面的实践。采用建表过程中，直接：stored as orc，就可以指定。然而用传统文本文件导入的方式，再进行查询测试，如select count(*) from table XX. 则会出现：Failed with exception java.io.IOException:java.大数据">
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If using Keras pass *_constraint arguments to layers.
Traceback (most recent call last):
  File &quot;neural_style.py&quot;, line 205, in &lt;module&gt;
    main()
  File &quot;neural_style.py&quot;, line 176, in main
    checkpoint_iterations=options.checkpoint_iterations
  File &quot;C:\Users\Administrator\stylize.py&quot;, line 117, in stylize
    _, height, width, number = map(lambda j: j.value, layer.get_shape())   # 117
  File &quot;C:\Users\Administrator\stylize.py&quot;, line 117, in &lt;lambda&gt;
    _, height, width, number = map(lambda j: j.value, layer.get_shape())   # 117
AttributeError: &#039;int&#039; object has no attribute &#039;value&#039;    请问为什么  将j换为i还是报同样的错误                </p>
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                <h1 class="title-article">Hive ORC文件格式存储与测试（Malformed ORC file解决）</h1>
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                                                                                                                                            <a class="follow-nickName" href="https://me.csdn.net/sparkexpert" target="_blank" rel="noopener">sparkexpert</a>
                    <span class="time">2015-12-06 10:39:52</span>
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                                <div class="up-time"><span>最后发布:2015-12-06 10:39:52</span><span>首发:2015-12-06 10:39:52</span></div>
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<p>Hive本身支持好多种数据格式，其中OrcFile是当前压缩比最大的一个。因此进行该方面的实践。</p>
<p><br /></p>
<p>采用建表过程中，直接：stored as orc，就可以指定。</p>
<p><br /></p>
<p>然而用传统文本文件导入的方式，再进行查询测试，如select count(*) from table XX. 则会出现：<span style="color:rgb(34,34,34);font-family:Consolas, Menlo, Monaco, 'Lucida Console', 'Liberation Mono', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Courier New', monospace, sans-serif;font-size:13px;line-height:16.9px;">Failed
 with exception java.io.IOException:java.io.IOException: Malformed ORC file的问题。</span></p>
<p><span style="color:rgb(34,34,34);font-family:Consolas, Menlo, Monaco, 'Lucida Console', 'Liberation Mono', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Courier New', monospace, sans-serif;font-size:13px;line-height:16.9px;"><br /></span></p>
<p><span style="font-family:Consolas, Menlo, Monaco, 'Lucida Console', 'Liberation Mono', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Courier New', monospace, sans-serif;color:#222222;"><span style="font-size:13px;line-height:16.9px;">找到解决办法，由于TXT文档导入，无法生成ORC数据结构，所以需要先导入临时表，再从临时表中再导到ORC表中。</span></span></p>
<p><span style="font-family:Consolas, Menlo, Monaco, 'Lucida Console', 'Liberation Mono', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Courier New', monospace, sans-serif;color:#222222;"><span style="font-size:13px;line-height:16.9px;">命令如下：INSERT
 INTO TABLE XX partition(year=2015,month=12,day=1,hour=0) SELECT * FROM XXX;</span></span></p>
<p><span style="font-family:Consolas, Menlo, Monaco, 'Lucida Console', 'Liberation Mono', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Courier New', monospace, sans-serif;color:#222222;"><span style="font-size:13px;line-height:16.9px;"><br /></span></span></p>
<p><span style="font-family:Consolas, Menlo, Monaco, 'Lucida Console', 'Liberation Mono', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Courier New', monospace, sans-serif;color:#222222;"><span style="font-size:13px;line-height:16.9px;">这样的话，可以明显看到里面有三个步骤。需要进行三次mapreduce.</span></span></p>
<p><img src="https://img-blog.csdn.net/20151206104625998?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" alt="" /><br /></p>
<p><br /></p>
<p>插入结果，对比大小，还是十分令人振奋的。从200多M，直接变为几K。</p>
<p><img src="https://img-blog.csdn.net/20151206104630958?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" alt="" /><br /></p>
<p><br /></p>
<p>而且进行查询，发现查询速度还是比普通的提高很多！</p>
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